numpy.ma.fromflex#

ma.fromflex(fxarray)[source]#

Build a masked array from a suitable flexible-type array.

The input array has to have a data-type with _data and _mask fields. This type of array is output by MaskedArray.toflex.

Parameters:

fxarrayndarray: The structured input array, containing _data and _mask fields. If present, other fields are discarded.

Returns:

resultMaskedArray: The constructed masked array.

See also

MaskedArray.toflex: Build a flexible-type array from a masked array.

Examples

>>> import numpy as np
>>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)
>>> rec = x.toflex()
>>> rec
array([[(0, False), (1,  True), (2, False)],
       [(3,  True), (4, False), (5,  True)],
       [(6, False), (7,  True), (8, False)]],
      dtype=[('_data', '<i8'), ('_mask', '?')])
>>> x2 = np.ma.fromflex(rec)
>>> x2
masked_array(
  data=[[0, --, 2],
        [--, 4, --],
        [6, --, 8]],
  mask=[[False,  True, False],
        [ True, False,  True],
        [False,  True, False]],
  fill_value=999999)

Extra fields can be present in the structured array but are discarded:

>>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]
>>> rec2 = np.zeros((2, 2), dtype=dt)
>>> rec2
array([[(0, False, 0.), (0, False, 0.)],
       [(0, False, 0.), (0, False, 0.)]],
      dtype=[('_data', '<i4'), ('_mask', '?'), ('field3', '<f4')])
>>> y = np.ma.fromflex(rec2)
>>> y
masked_array(
  data=[[0, 0],
        [0, 0]],
  mask=[[False, False],
        [False, False]],
  fill_value=np.int64(999999),
  dtype=int32)